Abstract
The sole method available to humans for precise control of power energy is power electronic technology, which is also a key trend in developing the future power system and the entire energy ...structure. PWM control technology regulates the pulse signal’s width, and this circuit can alter the fundamental amplitude and form of the input voltage. This study will evaluate the three-phase inverter circuit’s operating principle, develop its control strategy, create a SIMULINK simulation model, and do a rough analysis using an LC filter.
This paper investigates the sensitivity of forecast performance metrics to taking a real time versus pseudo out‐of‐sample perspective. I use monthly vintages of two popular datasets for the United ...States and the euro area. Variants of vector autoregressions, varying the size of the information sets and the conditional mean and variance specification, are considered. The results suggest differences in the relative ordering of model performance for point and density forecasts depending on the forecast simulation design used to evaluate predictive accuracy. Differentials are more pronounced for the European dataset, and stochastic volatility is a particularly attractive model feature to obtain accurate forecasts in real time.
Abstract
Intermittent arcing often occurs when a single-phase-to-ground fault occurs in the distribution network. However, the intermittent fault modeling suitable for distribution network fault ...analysis is not perfect, the ability to handle intermittent arcs is insufficient, and fault line selection is prone to misjudgment. In this paper, based on analyzing the operating voltage and current characteristics of intermittent faults in the resonant grounding system of the distribution network, a simulation model of intermittent grounding faults of the 10kV distribution network is established in PSCAD/EMTDC, and a new method based on transient characteristics is proposed. The line selection method for intermittent faults in the distribution network based on fault transient characteristics is proposed. The simulation results show that the established model is suitable for fault analysis of distribution networks, and the proposed method of fault line selection is fast and correct.
The aim of this paper is to assess whether modeling structural change can help improving the accuracy of macroeconomic forecasts. We conduct a simulated real-time out-of-sample exercise using a ...time-varying coefficients vector autoregression (VAR) with stochastic volatility to predict the inflation rate, unemployment rate and interest rate in the USA. The model generates accurate predictions for the three variables. In particular, the forecasts of inflation are much more accurate than those obtained with any other competing model, including fixed coefficients VARs, time-varying autoregressions and the naïve random walk model. The results hold true also after the mid 1980s, a period in which forecasting inflation was particularly hard.
This article provides a simple shrinkage representation that describes the operational characteristics of various forecasting methods designed for a large number of orthogonal predictors (such as ...principal components). These methods include pretest methods, Bayesian model averaging, empirical Bayes, and bagging. We compare empirically forecasts from these methods with dynamic factor model (DFM) forecasts using a U.S. macroeconomic dataset with 143 quarterly variables spanning 1960-2008. For most series, including measures of real economic activity, the shrinkage forecasts are inferior to the DFM forecasts. This article has online supplementary material.
We model US post-WWII monthly data with a Smooth Transition VAR model and study the effects of an unanticipated increase in economic policy uncertainty on unemployment in recessions and expansions. ...We find the response of unemployment to be statistically and economically larger in recessions. A state-contingent forecast error variance decomposition analysis confirms that the contribution of EPU shocks to the volatility of unemployment at business cycle frequencies is markedly larger in recessions.
We document five novel empirical findings on the well-known potential ordering drawback associated with the time-varying parameter vector autoregression with stochastic volatility developed by Cogley ...and Sargent (2005) and Primiceri (2005). First, the ordering does not affect point prediction. Second, the standard deviation of the predictive densities implied by different orderings can differ substantially. Third, the average length of the prediction intervals is also sensitive to the ordering. Fourth, the best ordering for one variable in terms of log-predictive scores does not necessarily imply the best ordering for another variable under the same metric. Fifth, the ordering problem becomes exacerbated in conditional forecasting exercises. Then, we consider three alternative ordering invariant models: a canonical discounted Wishart stochastic volatility model and two dynamic stochastic correlation models. When the forecasting performance of these ordering invariant models is compared to Cogley, Primiceri, and Sargent’s ordering variant model, the former underperforms relative to all orderings and the latter two have an out-of-sample forecasting performance comparable with the median outcomes across orderings.
In a data-rich environment, forecasting economic variables amounts to extracting and organizing useful information from a large number of predictors. So far, the dynamic factor model and its variants ...have been the most successful models for such exercises. In this paper, we investigate a category of LASSO-based approaches and evaluate their predictive abilities for forecasting twenty important macroeconomic variables. These alternative models can handle hundreds of data series simultaneously, and extract useful information for forecasting. We also show, both analytically and empirically, that combing forecasts from LASSO-based models with those from dynamic factor models can reduce the mean square forecast error (MSFE) further. Our three main findings can be summarized as follows. First, for most of the variables under investigation, all of the LASSO-based models outperform dynamic factor models in the out-of-sample forecast evaluations. Second, by extracting information and formulating predictors at economically meaningful block levels, the new methods greatly enhance the interpretability of the models. Third, once forecasts from a LASSO-based approach are combined with those from a dynamic factor model by forecast combination techniques, the combined forecasts are significantly better than either dynamic factor model forecasts or the naïve random walk benchmark.
There is strong evidence of structural changes in macroeconomic time series, and the forecasting performance is often sensitive to the choice of estimation window size. This paper develops a method ...for selecting the window size for forecasting. Our proposed method is to choose the optimal size that minimizes the forecaster’s quadratic loss function, and we prove the asymptotic validity of our approach. Our Monte Carlo experiments show that our method performs well under various types of structural changes. When applied to forecasting US real output growth and inflation, the proposed method tends to improve upon conventional methods, especially for output growth.
Abstract A mechanical parking system is an important parking equipment that can improve the utilization rate of urban space. In China, the lift-sliding mechanical parking system is the most widely ...used. Among many types of drives, the hydraulic pump-hydraulic cylinder type has advantages such as convenient system integration and expansion, energy saving, etc. The system is prone to vibration and pressure shock during operation. This paper focuses on the causes of vibration and hydraulic impact, constructs a simulation model, and analyzes the causes of vibration and hydraulic impact through simulation results, guiding equipment optimization.
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